Apart from image diversity, we also investigate the feature accuracy of the generated images of our method.
We synthesise 200 images in each class and perform do a user study.
Table REF  shows the consequences of whether the user thinks the images belong to their categories.
We can see the “young female" class has the highest accuracy.
The reasons behind this are various.
First, the dataset used to train StyleGAN[1]} is imbalanced in each class.
From the CelebA[2]} Database notes we deduce that this dataset has a larger percentage of “young female" images, leading to the subspace of “young female" in the hyper coordinate system being larger than others.
